# libraries
library(here)
library(readr)
library(DT)
# variables
url_ac <- "https://oceanview.pfeg.noaa.gov/erddap/tabledap/cciea_AC.csv"
# if ERDDAP server down (Error in download.file) with URL above, use this:
# url_ac <- "https://raw.githubusercontent.com/noaa-iea/r3-train/master/data/cciea_AC.csv"
csv_ac <- here("data/cciea_AC.csv")
# download data
if (!file.exists(csv_ac))
download.file(url_ac, csv_ac)
# read data
d_ac <- read_csv(csv_ac, col_names = F, skip = 2)
##
## -- Column specification -------------------------------------------------------------
## cols(
## .default = col_double(),
## X1 = col_datetime(format = "")
## )
## i Use `spec()` for the full column specifications.
names(d_ac) <- names(read_csv(csv_ac))
##
## -- Column specification -------------------------------------------------------------
## cols(
## .default = col_double(),
## time = col_datetime(format = "")
## )
## i Use `spec()` for the full column specifications.
# show data
datatable(d_ac)
library(dplyr)
library(ggplot2)
# subset data
d_coast <- d_ac %>%
# select columns
select(time, total_fisheries_revenue_coastwide) %>%
# filter rows
filter(!is.na(total_fisheries_revenue_coastwide))
datatable(d_coast)
# ggplot object
p_coast <- d_coast %>%
# setup aesthetics
ggplot(aes(x = time, y = total_fisheries_revenue_coastwide)) +
# add geometry
geom_line()
# show plot
p_coast

p_coast +
geom_smooth(method = "lm")
## `geom_smooth()` using formula 'y ~ x'

d_coast %>%
# setup aesthetics
ggplot(aes(x = total_fisheries_revenue_coastwide)) +
# add geometry
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

library(stringr)
library(tidyr)
d_rgn <- d_ac %>%
# select columns
select(
time,
starts_with("total_fisheries_revenue")) %>%
# exclude column
select(-total_fisheries_revenue_coastwide) %>%
# pivot longer
pivot_longer(-time) %>%
# mutate region by stripping other
mutate(
region = name %>%
str_replace("total_fisheries_revenue_", "") %>%
str_to_upper()) %>%
# filter for not NA
filter(!is.na(value)) %>%
# select columns
select(time, region, value)
# create plot object
p_rgn <- ggplot(
d_rgn,
# aesthetics
aes(
x = time,
y = value,
group = region,
color = region)) +
# geometry
geom_line()
# show plot
p_rgn

p_rgn <- p_rgn +
labs(
title = "Fisheries Revenue",
x = "Year",
y = "Millions $ (year 2015)",
color = "Region")
p_rgn

p_rgn +
facet_wrap(vars(region))

library(glue)
library(lubridate)
yr_max <- year(max(d_rgn$time))
d_rgn %>%
# filter by most recent time
filter(year(time) == yr_max) %>%
# setup aesthetics
ggplot(aes(x = region, y = value, fill = region)) +
# add geometry
geom_col() +
# add labels
labs(
title = glue("Fisheries Revenue for {yr_max}"),
x = "Region",
y = "Millions $ (year 2015)",
fill = "Region")

d_rgn %>%
# setup aesthetics
ggplot(aes(x = region, y = value, fill = region)) +
# add geometry
geom_boxplot() +
# add labels
labs(
title = "Fisheries Revenue Variability",
x = "Region",
y = "Millions $ (year 2015)") +
# drop legend since redundant with x axis
theme(
legend.position = "none")

p_rgn_violin <- d_rgn %>%
# setup aesthetics
ggplot(aes(x = region, y = value, fill = region)) +
# add geometry
geom_violin() +
# add labels
labs(
title = "Fisheries Revenue Variability",
x = "Region",
y = "Millions $ (year 2015)") +
# drop legend since redundant with x axis
theme(
legend.position = "none")
p_rgn_violin

p_rgn_violin +
theme_classic()

plotly::ggplotly(p_rgn)
library(dygraphs)
d_rgn_wide <- d_rgn %>%
mutate(
Year = year(time)) %>%
select(Year, region, value) %>%
pivot_wider(
names_from = region,
values_from = value)
datatable(d_rgn_wide)
d_rgn_wide %>%
dygraph() %>%
dyRangeSelector()